Gastos Casa
28 agosto, 2023
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 8/7/2023 | Agua | 12706 | Andrés | NA |
| 8/7/2023 | Comida | 57693 | Tami | Supermercado |
| 9/7/2023 | correo | 8000 | Andrés | correos de chile raul miranda |
| 9/7/2023 | mouse | 51980 | Andrés | NA |
| 9/7/2023 | lamina | 13800 | Andrés | NA |
| 12/7/2023 | Comida | 26780 | Andrés | NA |
| 12/7/2023 | Netflix | 11880 | Tami | Netflix junio y julio 2023 |
| 17/7/2023 | Comida | 86974 | Tami | Supermercado |
| 19/7/2023 | VTR | 21990 | Andrés | NA |
| 25/7/2023 | Comida | 75171 | Tami | Supermercado |
| 24/7/2023 | Enceres | 27065 | Andrés | secador platos |
| 30/7/2023 | Comida | 19630 | Andrés | choritos, costa rama y weas |
| 30/7/2023 | Parafina | 19920 | Tami | NA |
| 30/7/2023 | Electricidad | 49345 | Andrés | PAC ENEL 01686518 |
| 31/7/2023 | Comida | 78380 | Tami | Supermercado |
| 3/8/2023 | Comida | 19000 | Andrés | NA |
| 3/8/2023 | Diosi | 15980 | Andrés | NA |
| 6/8/2023 | Gas | 16650 | Andrés | NA |
| 6/8/2023 | Gas | 23666 | Tami | Parafina |
| 8/8/2023 | Comida | 78577 | Tami | Supermercado |
| 9/8/2023 | Agua | 11520 | Andrés | NA |
| 15/8/2023 | Comida | 51910 | Tami | Supermercado |
| 16/8/2023 | Bencina + peajes Maite | 49000 | Tami | NA |
| 16/8/2023 | Comida | 13500 | Tami | Maitemarket |
| 20/8/2023 | VTR | 21990 | Andrés | NA |
| 21/8/2023 | Comida | 99535 | Tami | NA |
| 21/8/2023 | Comida | 27680 | Andrés | nueces almendras |
| 26/8/2023 | Comida | 71467 | Tami | Supermercado |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 7.0747e+08 2 7.3829 7e-04 ***
## lag_depvar 8.4575e+10 1 1765.1952 <2e-16 ***
## Residuals 2.9226e+10 610
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 1135.764 13321.91 0.0151167
## 2-0 28577.061 23027.029 34127.09 0.0000000
## 2-1 21348.223 18060.035 24636.41 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
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## 19 22034.57 0 13927.00
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## 21 20390.57 0 20986.00
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## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
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## 276 63044.86 2 69999.29
## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
## 486 27410.43 2 34690.43
## 487 41755.00 2 27410.43
## 488 49379.57 2 41755.00
## 489 57198.86 2 49379.57
## 490 51144.57 2 57198.86
## 491 56677.43 2 51144.57
## 492 65416.43 2 56677.43
## 493 69779.71 2 65416.43
## 494 54046.00 2 69779.71
## 495 43259.57 2 54046.00
## 496 40998.57 2 43259.57
## 497 41368.57 2 40998.57
## 498 42274.29 2 41368.57
## 499 35962.71 2 42274.29
## 500 38709.00 2 35962.71
## 501 44778.14 2 38709.00
## 502 51282.43 2 44778.14
## 503 52094.86 2 51282.43
## 504 52221.43 2 52094.86
## 505 45011.43 2 52221.43
## 506 46545.43 2 45011.43
## 507 42263.00 2 46545.43
## 508 45417.43 2 42263.00
## 509 45034.71 2 45417.43
## 510 37840.57 2 45034.71
## 511 39135.43 2 37840.57
## 512 38191.14 2 39135.43
## 513 39456.86 2 38191.14
## 514 42479.14 2 39456.86
## 515 34282.57 2 42479.14
## 516 28878.43 2 34282.57
## 517 56227.14 2 28878.43
## 518 65569.43 2 56227.14
## 519 69751.29 2 65569.43
## 520 62171.71 2 69751.29
## 521 63705.14 2 62171.71
## 522 79257.86 2 63705.14
## 523 87244.71 2 79257.86
## 524 58568.00 2 87244.71
## 525 52695.29 2 58568.00
## 526 48911.00 2 52695.29
## 527 53924.00 2 48911.00
## 528 53358.86 2 53924.00
## 529 42121.14 2 53358.86
## 530 47835.71 2 42121.14
## 531 62329.29 2 47835.71
## 532 56056.86 2 62329.29
## 533 59946.43 2 56056.86
## 534 64511.57 2 59946.43
## 535 61137.43 2 64511.57
## 536 55448.71 2 61137.43
## 537 47964.43 2 55448.71
## 538 46425.71 2 47964.43
## 539 55512.00 2 46425.71
## 540 55226.29 2 55512.00
## 541 46709.14 2 55226.29
## 542 49254.71 2 46709.14
## 543 49056.29 2 49254.71
## 544 49850.57 2 49056.29
## 545 39145.71 2 49850.57
## 546 29799.43 2 39145.71
## 547 34769.86 2 29799.43
## 548 44061.57 2 34769.86
## 549 43829.14 2 44061.57
## 550 45782.00 2 43829.14
## 551 38924.57 2 45782.00
## 552 49242.43 2 38924.57
## 553 50565.00 2 49242.43
## 554 38864.43 2 50565.00
## 555 49786.71 2 38864.43
## 556 58787.86 2 49786.71
## 557 58060.86 2 58787.86
## 558 62179.43 2 58060.86
## 559 57333.86 2 62179.43
## 560 70797.00 2 57333.86
## 561 89901.71 2 70797.00
## 562 78558.14 2 89901.71
## 563 65466.00 2 78558.14
## 564 70525.00 2 65466.00
## 565 68377.86 2 70525.00
## 566 69736.29 2 68377.86
## 567 60085.86 2 69736.29
## 568 41757.00 2 60085.86
## 569 49780.29 2 41757.00
## 570 56540.29 2 49780.29
## 571 57894.29 2 56540.29
## 572 60270.29 2 57894.29
## 573 61011.00 2 60270.29
## 574 57721.43 2 61011.00
## 575 71741.00 2 57721.43
## 576 59576.00 2 71741.00
## 577 52390.29 2 59576.00
## 578 61092.29 2 52390.29
## 579 62814.00 2 61092.29
## 580 54908.29 2 62814.00
## 581 62082.00 2 54908.29
## 582 57017.71 2 62082.00
## 583 53634.43 2 57017.71
## 584 69169.00 2 53634.43
## 585 52488.14 2 69169.00
## 586 60895.57 2 52488.14
## 587 59856.57 2 60895.57
## 588 52670.00 2 59856.57
## 589 51874.57 2 52670.00
## 590 52190.57 2 51874.57
## 591 41562.43 2 52190.57
## 592 44764.14 2 41562.43
## 593 38612.71 2 44764.14
## 594 43473.14 2 38612.71
## 595 53505.00 2 43473.14
## 596 45870.86 2 53505.00
## 597 52578.00 2 45870.86
## 598 55300.00 2 52578.00
## 599 61789.71 2 55300.00
## 600 57391.71 2 61789.71
## 601 62902.29 2 57391.71
## 602 53250.43 2 62902.29
## 603 55402.57 2 53250.43
## 604 56291.29 2 55402.57
## 605 58933.57 2 56291.29
## 606 59590.71 2 58933.57
## 607 59065.00 2 59590.71
## 608 52399.57 2 59065.00
## 609 60483.43 2 52399.57
## 610 58262.71 2 60483.43
## 611 54939.71 2 58262.71
## 612 51169.00 2 54939.71
## 613 43113.29 2 51169.00
## 614 56289.71 2 43113.29
## 615 60739.86 2 56289.71
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 458 50811.32 14966.166
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 1955.70379 4013.29769 -511.16189 2461.40011 -2916.41883 538.08747
## 8 9 10 11 12 13
## -5627.41072 -1219.80522 -4001.46573 -486.94330 -4998.83767 -1709.93308
## 14 15 16 17 18 19
## -999.61152 286.06177 -3312.76711 -469.04576 -2208.11674 6518.20745
## 20 21 22 23 24 25
## -1525.59818 -1216.31653 1460.99225 -1177.31436 235.39952 1704.02879
## 26 27 28 29 30 31
## -7069.62506 903.22320 8170.03943 495.22047 63.84693 -2326.80939
## 32 33 34 35 36 37
## 1620.01909 4634.13676 1237.27848 2506.28633 -1735.25190 4709.20345
## 38 39 40 41 42 43
## 4363.48977 -2175.09137 -2918.16069 -1087.31469 -10733.27798 7178.18591
## 44 45 46 47 48 49
## 2541.18627 1381.56353 8133.62901 801.16162 6637.35458 6882.00535
## 50 51 52 53 54 55
## -5661.09452 -4666.64136 -4999.69522 -7931.54932 6040.03128 -4086.80337
## 56 57 58 59 60 61
## -4947.83295 3755.39981 843.18495 -60.85322 117.22244 -5016.24167
## 62 63 64 65 66 67
## 18054.69428 3778.58700 -3484.80901 6026.54347 7498.55197 14855.72385
## 68 69 70 71 72 73
## 2045.21696 -12885.73059 -1165.79462 4752.34452 -4753.24930 -4329.58519
## 74 75 76 77 78 79
## -10479.97740 2366.94892 -5459.09958 953.01203 -6950.64644 398.09888
## 80 81 82 83 84 85
## -2477.93869 -2826.98856 -4077.26063 -707.25943 2160.99251 3654.51386
## 86 87 88 89 90 91
## 424.34040 -524.76684 156.58166 4269.58584 -1143.40587 1155.64038
## 92 93 94 95 96 97
## -2047.12015 -1051.39597 160.35984 262.17871 -7491.53715 2303.33984
## 98 99 100 101 102 103
## -8652.84763 -3078.59006 -4191.66875 -1913.38278 -1434.01878 3016.93939
## 104 105 106 107 108 109
## -2449.75309 2474.73587 -1233.54311 892.61359 2530.28465 -3175.04777
## 110 111 112 113 114 115
## -4775.26666 -947.26480 1809.99156 11633.31579 -1166.07519 2722.24557
## 116 117 118 119 120 121
## 4339.68752 3617.30227 -960.65574 -4606.12807 -3679.08373 2318.74279
## 122 123 124 125 126 127
## -1707.42527 1344.00460 8876.71665 961.29431 240.14140 -2423.39060
## 128 129 130 131 132 133
## 2713.44072 7133.32895 1161.23377 -8357.67053 1780.08850 4182.30208
## 134 135 136 137 138 139
## -3077.01735 -1378.02733 -832.59894 -3870.16049 1149.53417 -511.18088
## 140 141 142 143 144 145
## -2932.21893 1670.40412 -1903.36513 -7868.92437 1919.26192 -3561.78091
## 146 147 148 149 150 151
## 1992.74885 -329.53976 957.69636 -404.66894 1309.06977 1164.29130
## 152 153 154 155 156 157
## 3350.57814 -4829.63847 -1199.35119 -3269.96369 5891.79405 9755.91267
## 158 159 160 161 162 163
## -3443.26144 -4810.50435 3538.93539 192.96033 2712.03838 -5852.05225
## 164 165 166 167 168 169
## -6745.85993 4099.54453 17402.74167 3833.77390 -166.51752 -2233.87521
## 170 171 172 173 174 175
## -931.12543 3743.61311 -42.79467 -7902.34941 2942.67110 4438.25535
## 176 177 178 179 180 181
## 783.19081 8908.01756 -9005.23090 -3343.87957 -10656.44139 -11263.54442
## 182 183 184 185 186 187
## 1108.82261 9209.52141 -1392.12194 5959.44332 6656.48804 13325.89940
## 188 189 190 191 192 193
## 8723.26170 -3712.33290 2736.68202 10639.20429 -1293.23639 -2148.12237
## 194 195 196 197 198 199
## -10038.99262 -6249.28385 1277.41484 -5172.18041 -9785.76128 5303.46623
## 200 201 202 203 204 205
## -3071.22042 -1734.89914 -829.75764 6475.08199 9940.53347 736.90011
## 206 207 208 209 210 211
## 3077.15701 3269.04336 5973.51969 13065.65082 -5347.17184 -11047.87704
## 212 213 214 215 216 217
## -5553.96712 -10535.95641 -5125.86733 1443.19027 -13056.22778 16230.55307
## 218 219 220 221 222 223
## 7846.58309 1648.47885 26817.83959 12912.79328 7803.33582 14514.80606
## 224 225 226 227 228 229
## -3341.66334 -1274.27995 4174.72615 752.78171 3101.64952 9352.38824
## 230 231 232 233 234 235
## 6235.49347 -1481.81491 -1465.74236 9734.15010 -11134.35689 -7062.78799
## 236 237 238 239 240 241
## -8411.76465 -10064.08299 3019.48936 1343.55487 -8279.78025 -9046.36831
## 242 243 244 245 246 247
## 8966.70363 -7774.69100 2412.76652 -10330.12027 -4170.81499 1291.65124
## 248 249 250 251 252 253
## 914.00982 -12372.77570 3482.10869 1968.84066 4160.86468 2141.86132
## 254 255 256 257 258 259
## -1123.79119 11169.08819 21024.59430 3531.43575 -3943.32517 4359.81029
## 260 261 262 263 264 265
## -1429.89240 3958.14374 -4615.87872 -10728.10885 -4682.55899 -519.18797
## 266 267 268 269 270 271
## -5182.72838 8740.82228 -4221.03517 4206.78464 -2044.81633 4471.38621
## 272 273 274 275 276 277
## 793.03416 7388.95303 -1262.42107 12147.02894 -4361.09024 1879.63781
## 278 279 280 281 282 283
## -217.66175 7986.91202 -4862.00981 -2603.22694 -11169.16859 -2685.58100
## 284 285 286 287 288 289
## 18624.05126 7934.71128 2941.51496 -419.03583 1086.81780 6568.20007
## 290 291 292 293 294 295
## 7092.70674 -18523.03551 -11084.30152 -8164.09189 9566.17950 3083.04280
## 296 297 298 299 300 301
## -1133.10358 27440.16769 10347.99347 5240.16754 9860.73979 3242.99888
## 302 303 304 305 306 307
## -666.77877 8210.27699 -23947.81699 -3436.04534 -109.82138 -6902.11401
## 308 309 310 311 312 313
## -3963.24824 2917.77038 -9166.88163 -3273.72980 -8237.14419 1466.58112
## 314 315 316 317 318 319
## -3208.46938 1986.09997 -4105.43822 27403.72261 -536.14182 3454.66150
## 320 321 322 323 324 325
## 11006.79541 5840.07791 32651.87578 5627.02583 -20438.62638 2057.25478
## 326 327 328 329 330 331
## 1364.16593 -6226.53815 -1571.69970 -33130.52407 772.15923 -2366.83446
## 332 333 334 335 336 337
## -145.23317 -3191.16195 4061.59857 -403.09236 -6905.73467 -3111.12739
## 338 339 340 341 342 343
## -2190.22129 -7673.89910 3816.77630 -1348.90752 -1708.28736 -960.98227
## 344 345 346 347 348 349
## 218.42501 540.20088 -1543.64467 -9374.93362 -13203.87488 2230.46094
## 350 351 352 353 354 355
## -4352.26940 -3696.58370 -6020.25417 1690.10738 1366.52736 2768.16232
## 356 357 358 359 360 361
## -3713.65562 -482.22816 720.99412 7074.56749 400.61239 87.65735
## 362 363 364 365 366 367
## 2708.18747 -2605.52666 -755.77259 -8626.58193 -4571.27656 -6178.97139
## 368 369 370 371 372 373
## -4945.83432 -7264.70755 4971.85474 391.61540 7159.79804 -7529.72158
## 374 375 376 377 378 379
## -2208.37756 -3336.77531 -2426.93102 -12419.74057 1863.95924 -10631.73030
## 380 381 382 383 384 385
## 5640.26817 9350.89182 3226.49384 -2271.66828 1713.72281 6867.10417
## 386 387 388 389 390 391
## 11582.02762 -5558.20527 -5185.56745 -32.37940 8683.43960 1993.74884
## 392 393 394 395 396 397
## 11400.37821 -9633.13419 2920.96185 869.76896 713.39459 -508.95515
## 398 399 400 401 402 403
## -432.17197 -14367.25003 8539.93065 -1084.47265 -1280.60846 7068.45686
## 404 405 406 407 408 409
## -7791.30390 -1214.00575 -2449.19565 -5745.00696 -2813.93779 -3875.44337
## 410 411 412 413 414 415
## -8723.96005 6121.89691 1695.82631 -7293.56261 -7654.18632 14221.94407
## 416 417 418 419 420 421
## 3934.40385 4634.98243 -7867.17005 -4637.82506 -2522.00295 2891.47366
## 422 423 424 425 426 427
## -13908.51898 -2779.85108 -9084.81082 2985.04089 6997.12912 6661.78484
## 428 429 430 431 432 433
## -3848.58529 -4009.46458 -4634.14537 -1725.08000 -5646.78412 -6588.55482
## 434 435 436 437 438 439
## -5940.77934 -1404.49063 -844.09029 -4955.05249 2586.54958 4884.12248
## 440 441 442 443 444 445
## -4963.08477 -2091.87916 1640.22985 -3749.29040 2905.83490 -6475.99382
## 446 447 448 449 450 451
## -12047.38089 -4520.58266 9628.97014 -1953.14520 4830.83657 -5746.71790
## 452 453 454 455 456 457
## -1036.11660 473.65867 3129.60791 -12134.83039 3418.08337 -6611.13254
## 458 459 460 461 462 463
## 6574.46037 3127.98960 2651.14139 -3681.26659 2228.78086 147.43053
## 464 465 466 467 468 469
## 1948.36963 -352.37855 3513.85624 -2452.36016 5969.15484 -6736.06318
## 470 471 472 473 474 475
## -2813.67785 -2072.48400 -4540.40484 3093.83442 7927.78298 -5824.00296
## 476 477 478 479 480 481
## 1630.53516 -6017.52106 -2726.00118 2118.25690 -12798.50682 -9713.28543
## 482 483 484 485 486 487
## -1213.81353 26.93163 -930.94916 -1296.92969 -9530.94292 11094.83188
## 488 489 490 491 492 493
## 6342.86891 7583.65411 -5217.13212 5539.37884 9504.61498 6327.86824
## 494 495 496 497 498 499
## -13170.50057 -10381.83859 -3336.27932 -1015.48251 -429.00520 -7522.02933
## 500 501 502 503 504 505
## 669.89701 4369.53724 5637.35035 837.86390 263.46980 -7055.73637
## 506 507 508 509 510 511
## 699.07082 -4906.89704 1942.42230 -1161.94135 -8025.87730 -523.89456
## 512 513 514 515 516 517
## -2585.38646 -504.93995 1425.28411 -9378.92347 -7711.04269 24300.38043
## 518 519 520 521 522 523
## 10046.12269 6167.43086 -5020.25804 3052.84493 17282.51296 11850.44684
## 524 525 526 527 528 529
## -23717.34904 -4847.71794 -3565.01014 4713.08164 -177.29095 -10927.39848
## 530 531 532 533 534 535
## 4483.10274 14046.12673 -4731.39383 4570.04566 5779.25594 -1533.70432
## 536 537 538 539 540 541
## -4311.19917 -6887.24707 -1968.49973 8445.39277 320.00699 -7950.62082
## 542 543 544 545 546 547
## 1943.56413 -451.19024 514.30017 -10875.86880 -10985.97529 2048.45346
## 548 549 550 551 552 553
## 7051.66875 -1197.67563 955.72151 -7586.63740 8647.82736 1068.12419
## 554 555 556 557 558 559
## -11773.56539 9244.00449 8821.37017 328.16016 5073.98913 -3325.09669
## 560 561 562 563 564 565
## 14318.81809 21807.49690 -6019.67285 -9324.55210 7030.38359 518.32208
## 566 567 568 569 570 571
## 3729.31122 -7093.17314 -17095.61483 6741.85759 6579.34533 2100.79934
## 572 573 574 575 576 577
## 3308.56454 1999.25970 -1929.40198 14928.42043 -9332.70310 -6022.42302
## 578 579 580 581 582 583
## 8879.43038 3093.03592 -6298.17804 7696.60757 -3557.17792 -2570.98444
## 584 585 586 587 588 589
## 15882.69490 -14201.43180 8598.28470 305.33295 -5984.78651 -579.62213
## 590 591 592 593 594 595
## 422.67576 -10478.11274 1893.59148 -7020.28470 3147.61281 8985.87925
## 596 597 598 599 600 601
## -7303.77664 5990.12499 2925.18429 7066.34973 -2930.99302 6374.18451
## 602 603 604 605 606 607
## -8032.20833 2447.58255 1479.42226 3354.92292 1732.29463 639.59574
## 608 609 610 611 612 613
## -5572.24515 8262.56150 -932.92623 -2339.88804 -3243.50908 -8045.84105
## 614 615
## 12081.07924 5162.56447
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17313.58 20125.70 24327.30 24048.74 26373.13 23738.63 24446.13 19736.95
## 10 11 12 13 14 15 16 17
## 19476.75 16852.23 17620.12 14389.79 14440.33 15096.80 16772.48 15113.19
## 18 19 20 21 22 23 24 25
## 16135.12 15516.36 22511.60 21606.89 21093.15 22959.89 22294.17 22938.69
## 26 27 28 29 30 31 32 33
## 24761.91 18765.06 20469.96 28210.78 28267.72 27944.67 25603.27 26988.43
## 34 35 36 37 38 39 40 41
## 30784.15 31128.29 32520.11 30061.37 34079.51 37248.09 34340.45 31190.60
## 42 43 44 45 46 47 48 49
## 30052.56 20748.10 28174.24 30580.72 31656.51 38410.41 37911.22 42515.99
## 50 51 52 53 54 55 56 57
## 46700.09 39487.93 34123.27 29207.26 22436.11 28648.66 25271.40 21614.60
## 58 59 60 61 62 63 64 65
## 25968.67 27212.71 27506.06 27912.81 23834.59 40221.56 42042.81 37347.31
## 66 67 68 69 70 71 72 73
## 41502.45 46357.56 56894.35 54932.59 40357.51 37894.08 40874.82 35245.16
## 74 75 76 77 78 79 80 81
## 30753.41 21571.34 24733.39 20709.27 22769.65 17728.04 19718.65 18954.70
## 82 83 84 85 86 87 88 89
## 17994.40 16087.12 17349.15 20912.77 25276.09 26253.77 26278.42 26887.56
## 90 91 92 93 94 95 96 97
## 30961.83 29806.79 30793.83 28882.11 28091.78 28455.39 28856.97 22513.52
## 98 99 100 101 102 103 104 105
## 25491.42 18607.73 17477.95 15542.81 15838.88 16507.92 20925.47 20020.26
## 106 107 108 109 110 111 112 113
## 23488.11 23280.67 24936.14 27777.48 25306.41 21793.69 22065.72 24679.40
## 114 115 116 117 118 119 120 121
## 35410.08 33625.18 35440.03 38401.41 40333.23 38050.13 32934.94 29321.40
## 122 123 124 125 126 127 128 129
## 31378.57 29679.71 30846.71 38352.85 37999.72 37072.82 33974.99 35734.24
## 130 131 132 133 134 135 136 137
## 41065.62 40512.81 31822.91 33072.13 36222.59 32677.46 31084.60 30180.87
## 138 139 140 141 142 143 144 145
## 26780.32 28177.32 27949.79 25664.60 27664.08 26305.78 19986.74 22979.92
## 146 147 148 149 150 151 152 153
## 20833.39 23773.83 24307.16 25877.95 26057.79 27691.57 28976.28 31971.07
## 154 155 156 157 158 159 160 161
## 27497.07 26769.11 24354.49 30175.94 41463.69 39814.50 37211.92 42170.33
## 162 163 164 165 166 167 168 169
## 43561.53 46935.34 42457.15 37822.17 43180.54 59281.80 61466.66 59900.30
## 170 171 172 173 174 175 176 177
## 56765.13 55184.10 57853.37 56889.49 49276.61 52065.32 55761.81 55797.55
## 178 179 180 181 182 183 184 185
## 62838.52 53457.88 50248.87 41170.83 32814.46 36279.48 46258.41 45721.13
## 186 187 188 189 190 191 192 193
## 51600.51 57274.67 67924.74 73142.48 66914.89 67105.94 74089.09 69818.84
## 194 195 196 197 198 199 200 201
## 65396.85 54773.28 48877.01 50283.75 45932.76 38198.11 44543.65 42792.90
## 202 203 204 205 206 207 208 209
## 42435.33 42907.78 49618.04 58397.67 58031.84 59735.39 61370.77 65115.21
## 210 211 212 213 214 215 216 217
## 74465.03 66645.45 54980.11 49655.38 40762.72 37757.95 40833.23 30976.45
## 218 219 220 221 222 223 224 225
## 47740.70 54971.24 55862.02 78346.78 85749.38 87727.91 95225.66 86288.14
## 226 227 228 229 230 231 232 233
## 80360.56 79947.65 76638.92 75810.75 80489.36 81836.81 76340.89 71612.85
## 234 235 236 237 238 239 240 241
## 77196.79 64009.22 56143.91 48193.80 39908.80 44049.02 46175.21 39706.65
## 242 243 244 245 246 247 248 249
## 33464.15 43619.83 37937.66 41824.83 34184.10 32905.92 36516.13 39305.20
## 250 251 252 253 254 255 256 257
## 30247.75 36112.59 39867.14 44997.85 47682.65 47181.48 57355.41 74636.85
## 258 259 260 261 262 263 264 265
## 74454.18 67847.33 69310.89 65578.28 67006.59 60841.25 50248.13 46324.47
## 266 267 268 269 270 271 272 273
## 46531.30 42686.03 51381.61 47700.64 51796.24 49936.04 53953.25 54245.62
## 274 275 276 277 278 279 280 281
## 60188.85 57852.26 67405.95 61405.65 61613.09 59982.52 65654.58 59462.37
## 282 283 284 285 286 287 288 289
## 56068.60 45749.72 44166.23 61186.00 66647.91 67052.32 64501.75 63600.37
## 290 291 292 293 294 295 296 297
## 67552.01 71414.04 52644.87 42868.95 36953.82 47147.96 50349.82 49474.69
## 298 299 300 301 302 303 304 305
## 73372.72 79244.83 79904.26 84459.86 82680.64 77772.15 81196.25 56404.47
## 306 307 308 309 310 311 312 313
## 52711.68 52395.40 46262.11 43505.94 47064.88 39708.87 38446.72 33075.28
## 314 315 316 317 318 319 320 321
## 36813.18 36004.61 39788.87 37798.13 63266.71 61134.48 62738.06 70637.64
## 322 323 324 325 326 327 328 329
## 72995.55 98163.26 96560.91 72688.89 71501.55 69879.11 61929.99 59087.67
## 330 331 332 333 334 335 336 337
## 29406.27 33048.41 33482.52 35773.88 35122.83 40818.81 41881.16 37187.27
## 338 339 340 341 342 343 344 345
## 36411.36 36536.47 31913.08 37838.19 38493.43 38748.70 39613.72 41377.66
## 346 347 348 349 350 351 352 353
## 43177.22 42931.93 35963.45 26647.40 31926.27 30801.30 30396.40 28042.18
## 354 355 356 357 358 359 360 361
## 32663.47 36371.55 40780.23 38991.51 40236.29 42348.43 49652.67 50196.49
## 362 363 364 365 366 367 368 369
## 50395.67 52828.53 50342.92 49794.30 42529.99 39761.26 35985.26 33791.28
## 370 371 372 373 374 375 376 377
## 29897.57 37095.81 39354.63 47143.15 41188.95 40642.92 39198.22 38736.74
## 378 379 380 381 382 383 384 385
## 29716.76 34258.30 27395.45 35513.68 45719.65 49241.24 47535.85 49503.04
## 386 387 388 389 390 391 392 393
## 55646.69 65015.49 58310.28 52846.52 52578.56 59867.39 60384.34 68946.42
## 394 395 396 397 398 399 400 401
## 58186.04 59733.66 59299.18 58789.38 57294.89 56071.68 42993.07 51473.19
## 402 403 404 405 406 407 408 409
## 50485.89 49464.83 55787.45 48421.58 47741.20 46088.44 41818.79 40663.87
## 410 411 412 413 414 415 416 417
## 38751.53 32918.25 40694.32 43584.71 38322.47 33471.06 48160.02 51957.59
## 418 419 420 421 422 423 424 425
## 55838.60 48400.25 44768.72 43460.95 47003.38 35564.71 35297.24 29626.53
## 426 427 428 429 430 431 432 433
## 35147.73 43373.07 50180.59 46985.75 44090.43 41053.37 40942.93 37463.98
## 434 435 436 437 438 439 440 441
## 33649.78 30917.78 32474.52 34301.20 32330.31 37136.73 43266.08 40058.31
## 442 443 444 445 446 447 448 449
## 39767.91 42737.43 40649.45 44589.99 39895.24 31037.58 29889.32 41106.86
## 450 451 452 453 454 455 456 457
## 40792.31 46374.15 42063.83 42409.20 44009.82 47682.40 37680.92 42470.70
## 458 459 460 461 462 463 464 465
## 37950.11 45426.30 48903.14 51491.55 48261.22 50573.28 50772.34 52497.95
## 466 467 468 469 470 471 472 473
## 52001.72 54909.36 52270.42 57259.63 50602.25 48242.48 46845.98 43511.74
## 474 475 476 477 478 479 480 481
## 47221.79 54593.57 49088.89 50771.24 45624.00 44022.89 46821.08 36365.14
## 482 483 484 485 486 487 488 489
## 30005.67 31852.07 34515.66 35987.36 36941.37 30660.17 43036.70 49615.20
## 490 491 492 493 494 495 496 497
## 56361.70 51138.05 55911.81 63451.85 67216.50 53641.41 44334.85 42384.05
## 498 499 500 501 502 503 504 505
## 42703.29 43484.74 38039.10 40408.61 45645.08 51256.99 51957.96 52067.16
## 506 507 508 509 510 511 512 513
## 45846.36 47169.90 43475.01 46196.66 45866.45 39659.32 40776.53 39961.80
## 514 515 516 517 518 519 520 521
## 41053.86 43661.49 36589.47 31926.76 55523.31 63583.85 67191.97 60652.30
## 522 523 524 525 526 527 528 529
## 61975.34 75394.27 82285.35 57543.00 52476.01 49210.92 53536.15 53048.54
## 530 531 532 533 534 535 536 537
## 43352.61 48283.16 60788.25 55376.38 58732.32 62671.13 59759.91 54851.68
## 538 539 540 541 542 543 544 545
## 48394.21 47066.61 54906.28 54659.76 47311.15 49507.48 49336.27 50021.58
## 546 547 548 549 550 551 552 553
## 40785.40 32721.40 37009.90 45026.82 44826.28 46511.21 40594.60 49496.88
## 554 555 556 557 558 559 560 561
## 50637.99 40542.71 49966.49 57732.70 57105.44 60658.95 56478.18 68094.22
## 562 563 564 565 566 567 568 569
## 84577.82 74790.55 63494.62 67859.54 66006.97 67179.03 58852.61 43038.43
## 570 571 572 573 574 575 576 577
## 49960.94 55793.49 56961.72 59011.74 59650.83 56812.58 68908.70 58412.71
## 578 579 580 581 582 583 584 585
## 52212.86 59720.96 61206.46 54385.39 60574.89 56205.41 53286.31 66689.57
## 586 587 588 589 590 591 592 593
## 52297.29 59551.24 58654.79 52454.19 51767.90 52040.54 42870.55 45633.00
## 594 595 596 597 598 599 600 601
## 40325.53 44519.12 53174.63 46587.88 52374.82 54723.36 60322.71 56528.10
## 602 603 604 605 606 607 608 609
## 61282.64 52954.99 54811.86 55578.65 57858.42 58425.40 57971.82 52220.87
## 610 611 612 613 614 615
## 59195.64 57279.60 54412.51 51159.13 44208.64 55577.29
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.834
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 7.382932 0.5669324 3.626872
## t2* 1765.195217 23.5653081 221.998164
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 2.843396 7.494069 14.52408
## 2 lag_depvar 1445.362227 1775.668255 2167.37328Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Aug 28 01:07:37 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Aug 28 01:07:47 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Aug 28 01:07:58 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Aug 28 01:08:09 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Aug 28 01:08:20 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Aug 28 01:08:31 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Aug 28 01:08:41 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Aug 28 01:08:52 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Aug 28 01:09:03 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Aug 28 01:09:14 2023
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | 3.630286 | 5.410333 | 5.629750 | 6.5064884 |
| Comida | 351.597429 | 310.278417 | 314.087500 | 342.1973488 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.0000000 |
| Electricidad | 39.311429 | 47.072333 | 38.297667 | 33.5250930 |
| Enceres | 26.902429 | 20.086417 | 17.443792 | 25.0026047 |
| Farmacia | 2.854286 | 1.831667 | 7.913875 | 8.7989302 |
| Gas/Bencina | 43.473714 | 44.325000 | 28.954333 | 28.0539535 |
| Diosi | 11.984286 | 31.180667 | 41.934250 | 35.7155349 |
| donaciones/regalos | 0.000000 | 0.000000 | 7.170083 | 6.3888140 |
| Electrodomésticos/ Mantención casa | 0.000000 | 3.944000 | 30.269500 | 19.2899535 |
| VTR | 12.567143 | 25.156667 | 22.121792 | 19.7269302 |
| Netflix | 5.771429 | 7.151583 | 7.090167 | 7.1983953 |
| Otros | 0.000000 | 3.151083 | 1.575542 | 0.8793721 |
| Total | 498.092429 | 499.588167 | 522.488250 | 533.2834186 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 41 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2078, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2023-09-09 00:04:58 sería de: 36.613 pesos// Percentil 95% más alto proyectado: 39.771,07
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 36179.82 | 36177.46 |
| Lo.80 | 36191.63 | 36187.51 |
| Point.Forecast | 36612.72 | 37560.89 |
| Hi.80 | 38384.88 | 42320.21 |
| Hi.95 | 39357.46 | 44839.64 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.2450 1007.9316
## s.e. 0.1372 29.9385
##
## sigma^2 = 28818: log likelihood = -352.89
## AIC=711.78 AICc=712.26 BIC=717.75paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.2217 714.6894 9.5292
## s.e. 0.1389 312.3797 10.0966
##
## sigma^2 = 28928: log likelihood = -352.46
## AIC=712.93 AICc=713.74 BIC=720.88forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 747.9152 | 664.7484 | 710.2107 |
| Lo.80 | 866.2449 | 783.5362 | 794.5381 |
| Point.Forecast | 1089.7748 | 1007.9316 | 982.1172 |
| Hi.80 | 1313.3048 | 1232.3271 | 1272.7341 |
| Hi.95 | 1431.6344 | 1351.1148 | 1459.9263 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 55 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.9 BoomSpikeSlab_1.2.5
## [4] Boom_0.9.11 scales_1.2.1 ggiraph_0.8.7
## [7] tidytext_0.4.1 DT_0.28 autoplotly_0.1.4
## [10] rvest_1.0.3 plotly_4.10.2 xts_0.13.1
## [13] forecast_8.21 wordcloud_2.6 RColorBrewer_1.1-3
## [16] SnowballC_0.7.1 tm_0.7-11 NLP_0.2-1
## [19] tsibble_1.1.3 lubridate_1.9.2 forcats_1.0.0
## [22] dplyr_1.1.2 purrr_1.0.1 tidyr_1.3.0
## [25] tibble_3.2.1 ggplot2_3.4.3 tidyverse_2.0.0
## [28] sjPlot_2.8.15 lattice_0.20-45 gridExtra_2.3
## [31] plotrix_3.8-2 sparklyr_1.8.2 httr_1.4.7
## [34] readxl_1.4.3 zoo_1.8-12 stringr_1.5.0
## [37] stringi_1.7.12 data.table_1.14.8 reshape2_1.4.4
## [40] fUnitRoots_4021.80 plyr_1.8.8 readr_2.1.4
##
## loaded via a namespace (and not attached):
## [1] uuid_1.1-0 backports_1.4.1 systemfonts_1.0.4
## [4] selectr_0.4-2 lazyeval_0.2.2 splines_4.1.2
## [7] crosstalk_1.2.0 digest_0.6.31 htmltools_0.5.5
## [10] fansi_1.0.4 ggfortify_0.4.16 magrittr_2.0.3
## [13] tzdb_0.4.0 modelr_0.1.11 vroom_1.6.3
## [16] timechange_0.2.0 anytime_0.3.9 tseries_0.10-54
## [19] colorspace_2.1-0 xfun_0.39 crayon_1.5.2
## [22] jsonlite_1.8.4 lme4_1.1-34 glue_1.6.2
## [25] gtable_0.3.4 emmeans_1.8.8 sjstats_0.18.2
## [28] sjmisc_2.8.9 car_3.1-2 quantmod_0.4.25
## [31] abind_1.4-5 mvtnorm_1.2-3 DBI_1.1.3
## [34] ggeffects_1.3.0 Rcpp_1.0.10 viridisLite_0.4.2
## [37] xtable_1.8-4 performance_0.10.4 bit_4.0.5
## [40] htmlwidgets_1.6.2 timeSeries_4031.107 gplots_3.1.3
## [43] ellipsis_0.3.2 spatial_7.3-14 pkgconfig_2.0.3
## [46] farver_2.1.1 nnet_7.3-16 sass_0.4.5
## [49] dbplyr_2.3.3 janitor_2.2.0 utf8_1.2.3
## [52] tidyselect_1.2.0 labeling_0.4.2 rlang_1.1.0
## [55] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
## [58] cachem_1.0.7 cli_3.6.1 generics_0.1.3
## [61] sjlabelled_1.2.0 broom_1.0.5 evaluate_0.20
## [64] fastmap_1.1.1 yaml_2.3.7 knitr_1.43
## [67] bit64_4.0.5 caTools_1.18.2 nlme_3.1-153
## [70] slam_0.1-50 xml2_1.3.3 tokenizers_0.3.0
## [73] compiler_4.1.2 rstudioapi_0.14 curl_5.0.2
## [76] bslib_0.4.2 highr_0.10 fBasics_4022.94
## [79] Matrix_1.6-1 its.analysis_1.6.0 nloptr_2.0.3
## [82] urca_1.3-3 vctrs_0.6.1 pillar_1.9.0
## [85] lifecycle_1.0.3 lmtest_0.9-40 jquerylib_0.1.4
## [88] estimability_1.4.1 bitops_1.0-7 insight_0.19.3
## [91] R6_2.5.1 KernSmooth_2.23-20 janeaustenr_1.0.0
## [94] codetools_0.2-18 assertthat_0.2.1 boot_1.3-28
## [97] MASS_7.3-54 gtools_3.9.4 withr_2.5.0
## [100] fracdiff_1.5-2 bayestestR_0.13.1 parallel_4.1.2
## [103] hms_1.1.3 quadprog_1.5-8 timeDate_4022.108
## [106] minqa_1.2.5 snakecase_0.11.1 rmarkdown_2.24
## [109] carData_3.0-5 TTR_0.24.3 base64enc_0.1-3#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))